The ml folder contains scripts implementing various machine learning algorithms using scikit-learn.
Description: Logistic regression and random forest for Iris binary classification.
Dependencies: scikit-learn, pandas
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
iris = load_iris()
X = iris.data[iris.target != 2] # Binary classification
y = iris.target[iris.target != 2]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
Description: MLP classifier for Iris classification.
Dependencies: scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=500, random_state=42)
mlp.fit(X_train, y_train)
Description: Custom neural network for word length classification.
Dependencies: numpy
Code:
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
X = np.array([[len(word)] for word in ["cat", "dog", "bird", "elephant"]])
y = np.array([0, 0, 1, 1]) # 0: short, 1: long
weights = np.random.rand(1)
bias = np.random.rand(1)
learning_rate = 0.01
for _ in range(1000):
z = np.dot(X, weights) + bias
pred = sigmoid(z)
error = pred - y
weights -= learning_rate * np.dot(X.T, error)
bias -= learning_rate * error.sum()
Description: Self-Organizing Map (SOM) for character image clustering.
Dependencies: numpy, minisom
Code:
from minisom import MiniSom
import numpy as np
data = np.random.rand(100, 10) # Simulated character image features
som = MiniSom(10, 10, 10, sigma=0.5, learning_rate=0.5)
som.random_weights_init(data)
som.train_random(data, 100)
Description: SVM with RBF kernel approximation for Iris classification.
Dependencies: scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
svm = SVC(kernel='rbf', random_state=42)
svm.fit(X_train, y_train)
Description: Linear regression for housing price prediction.
Dependencies: scikit-learn, pandas
Code:
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pandas as pd
housing = fetch_california_housing()
X, y = housing.data, housing.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
lr = LinearRegression()
lr.fit(X_train, y_train)
Description: Decision tree classifier for Iris classification.
Dependencies: scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
dt = DecisionTreeClassifier(random_state=42)
dt.fit(X_train, y_train)
Description: KNN classifier for Iris classification with k-value analysis.
Dependencies: scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
Description: KMeans clustering for Iris dataset.
Dependencies: scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
iris = load_iris()
X = iris.data
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)
Description: SVM classifier on Mushroom dataset for edible/poisonous classification.
Dependencies: scikit-learn, pandas
Code:
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import pandas as pd
mushroom = pd.read_csv("mushrooms.csv")
mushroom_df = pd.DataFrame(mushroom)
label_encoders = {}
for column in mushroom_df.columns:
le = LabelEncoder()
mushroom_df[column] = le.fit_transform(mushroom_df[column])
label_encoders[column] = le
X = mushroom_df[['cap-shape', 'cap-color']].values
y = mushroom_df['class'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
svm = SVC(kernel='linear', random_state=42)
svm.fit(X_train, y_train)
Description: Random forest classifier for Iris classification with visualization.
Dependencies: scikit-learn, matplotlib
Code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)